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Which Term Most Clearly Describes A Medium
Winton Center for Risk Communication and Evidence, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
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Uncertainty is a natural part of knowledge, but in an age of contested expertise, many are reluctant to openly express uncertainty about what they know for fear of audience reaction. But what is the communicative effect of such epistemic uncertainty? Empirical research is widespread in many disciplines. This interdisciplinary review outlines and summarizes current practice and research across domains, combining statistical and psychological perspectives. It establishes a framework for communicating uncertainty in which we define three entities of uncertainty—facts, numbers, and science—and two levels of uncertainty: direct and indirect. A review of current practice provides a nine-item measure of direct uncertainty. We review attempts at indirect uncertainty coding in terms of the quality of the underlying evidence. We review the limited literature on the effects of communicating epistemic uncertainty on cognition, affect, trust, and decision making. Although there is some evidence that communicating epistemic uncertainty does not necessarily have a negative effect on audiences, the effect may vary between individuals and communication formats. Examples from economic statistics and climate change illustrate our framework for action. We conclude with advice to guide both communicators and future researchers in this important but so far neglected area.
Uncertainty is pervasive in the world and we experience it regularly in our daily lives. We can say we are uncertain when we can’t predict the future, can’t decide what to do, are uncertain about what something means, don’t know what happened, or just because of a general sense of doubt or anxiety. The Cambridge Dictionary’s broad definition above describes the term “uncertainty” in many of the ways it is used in ordinary speech.
In the scientific context, a large literature focuses on what is often called the “uncertainty of uncertainty” because of the fundamental uncertainty or randomness in the world, often expressed in terms of luck or chance. It usually refers to future events about which we are not certain. This form of uncertainty is an important part of both quantifiable and non-quantifiable future risk assessment, communication and management, and prominent examples are uncertain economic forecasts, climate change models and actuarial survival curves.
In contrast, our focus in this paper is on uncertainty about facts, figures, and science due to limited knowledge or ignorance—called epistemic uncertainty. Epistemic uncertainty generally, but not always, refers to past or present events that we do not currently know, but can at least theoretically know or determine.
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Such epistemic uncertainty is an essential part of every step of the scientific process: from the assumptions we make, to the observations we make, to extrapolations and generalizations. This means that all the knowledge on which decisions and policies are based—from medical evidence to government statistics—is covered by epistemic uncertainty of various kinds and degrees.
Risk assessment and communication about possible future events are well-established academic and professional disciplines. In addition to, for example, the pure uncertainty of roulette, future risk assessments generally also contain a strong element of epistemic uncertainty that additional knowledge will change our predictions: see the recent example of climate change. However, relatively little has been studied about communicating ‘pure’ epistemic uncertainty, even though clearly failing to do so can seriously harm decisions (see Box 1).
On September 24, 2002, the British government published a document entitled “Iraq’s Weapons of Mass Destruction: A British Government Assessment” [3]. This included claims that Iraq had programs to develop weapons of mass destruction and nuclear ambitions, and constituted a “case for war”. Since the 2003 invasion of Iraq, Iraq’s research team has found no weapons of mass destruction or attempts to renew its nuclear program.
Because of the apparent gaps between the document and subsequent factual findings, an independent investigation (the Butler Review) was conducted in 2004. Butler’s review concluded that while there had been no deliberate distortion of the report, it was an expression of uncertainty in the intelligence field to date. In the original non-public assessment, it was omitted or not clear in the public report.
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“We believe it is a serious weakness that the JIC’s warnings about the limits of intelligence, which underpin some of its decisions, were not clearly set out in the dossier.”
In the US, the Intelligence Community’s October 2002 National Intelligence Estimate (NIE) entitled “Iraq’s Continuing Programs for Weapons of Mass Destruction” [5] is a similarly challenging document. The US Senate Select Committee inquiry was more critical of this than the Butler Review in the UK, but its second conclusion was similar:
“Finding 2. The intelligence community did not accurately or adequately explain to policymakers the uncertainties behind the October 2002 national intelligence assessment decision.”
The removal of important expressions of uncertainty from both documents had a profound effect on public opinion and governments, and in Great Britain at least, the elimination of uncertainty was seen as the key to giving way to war.
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Recent claims that we live in a “post-truth” society [7] do not seem to encourage scientists and policy makers to feel free to express their uncertainty openly. Surveys indicate declining levels of trust in governments and institutions [8-10], although trust in science appears to remain high in both the UK and the US [11, 12]. Anecdotal experience among many scholars and policymakers tacitly suggests that communicating uncertainty can have negative consequences, such as signaling incompetence, encouraging critics, and reducing trust (e.g. [13]). On the contrary, an alternative point of view, proposed for example by the philosopher O’Neill [14], is that such transparency can build trust in authorities rather than undermine it.
To clarify which of these conflicting claims is true, it is necessary to collect and analyze empirical evidence on the communicative effects of uncertainty about facts, figures, and science. This process faces two main challenges. First, existing empirical research on the communication effects of epistemic uncertainty is limited. Second, “addressing epistemic uncertainty” can mean many different things. This could be a graph of the probability distribution of historical global temperature change, a range around an estimate of the number of tigers in India, or a statement about the uncertainty caused by poor quality evidence. , such as a tainted DNA test in a criminal court. . All of these differences can affect how the communication of uncertainty affects people.
In this paper, we present a coherent framework that aims to provide clarity and structure to the issues surrounding such communication. It combines a statistical approach to uncertainty measurement with a psychological perspective that emphasizes the importance of communication effects on audiences, and is informed both by analysis of empirical studies of these effects and by real-world examples of uncertainty communication from a variety of fields. Our goal is to provide guidance on how to communicate uncertainty honestly and clearly without losing trust and credibility, for the benefit of anyone who subsequently uses the information to form an opinion or make a decision.
Unlike many attempts at general uncertainty taxonomies, the framework proposed in this paper is specifically focused on the communication task: a comparison with other proposals is provided in the next section. Based on Lasswell’s venerable communication model [15], our framework addresses who communicates what, in what form, to whom, and to what effect, while recognizing relevant context as audience characteristics. This framework for fuzzy communication is shown in figure 1.
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Figure 1. A basic deconstruction of epistemic uncertainty communication based on Lasswell’s communication model [15]. Our emphasis in this paper – what, in what form and with what effect – is in bold.
People who assess uncertainty, who will usually be some sort of ‘expert’, such as individual scientists, scientific groups such as the Intergovernmental Panel on Climate Change (IPCC), or official bodies such as national statistical organizations. They are essentially “owners” of uncertainty.
People who do the communication, which may include technical experts, communication professionals and journalists, often acting on behalf of institutions.
Degree of expressed uncertainty: from direct uncertainty about the fact, to implicit uncertainty or distrust of the underlying science
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An expression of uncertainty, such as a full probability distribution or simply a brief mention that uncertainty exists
Characteristics of viewers, for example in terms of different levels of numeracy and (graphic) literacy, their expertise and knowledge of the field.
The audience’s relationship to the communication, such as whether the topic is controversial or emotionally charged for them
The audience’s relationship with the people communicating, including perceived credibility and whether there is trust or distrust between the audience and the communicator.
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The first three parts of this paper follow the list above, briefly describing the who before focusing on the what.
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